Inferences about population mean

What is inference about population?

Statistical inference is the process through which inferences about a population are made based on certain statistics calculated from a sample of data drawn from that population. Surveys are used to study characteristics of, and make generalizations about, populations.You analyze the sample and make conclutions about the mean of the population. For instance, you perform a survey to find the average IQ of 50 students and make conclution about the whole university. Or you find the average height of about 100 people and make conclution about the whole nation.

Let's begin with the confidence interval.

In survey research it is important to receive feedback that is most reflective of the population you are targeting. The more appropriate the sample size, the more accurate the results.

Using a sample size that is too low will lessen quality and a sample size that is too large complicates analysis and is not time or cost efficient. Fortunately, determining sample size is not a guessing game and can be achieved using a simple calculation.

Your minimum sample size is the minimum number of respondents you need to get survey results that reflect the population you are studying, whilst adhering to your desired confidence interval (margin of error) and confidence level. To ensure more accurate results, you may need to increase your sample size as occurrences of “non-response” can cause biased results.

What Is Hypothesis Testing?

Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter.

In hypothesis testing, an analyst tests a statistical sample, with the goal of providing evidence on the plausibility of the null hypothesis.

In a hypothesis test, you have to decide if a claim is true or not. At first you should figure out if you have a left tailed test or right tailed test or two tailed test. A tail in hypothesis testing refers to the tail at either end of a distribution curve.
The right tailed test and the left tailed test are examples of one-tailed tests. They are called “one tailed” tests because the rejection region (the area where you would reject the null hypothesis) is only in one tail. The two tailed test is called a two tailed test because the rejection region can be in either tail.